Comparison of coding of heart failure and comorbidities in administrative and clinical data for use in outcomes research.

BACKGROUND

Despite the potential usefulness of administrative databases for evaluating outcomes, coding of heart failure and associated comorbidities have not been definitively compared with clinical data.

OBJECTIVE

To compare the predictive value of heart failure diagnoses and secondary conditions identified in a large administrative database with chart-based records.

METHODS

The authors studied 1808 patient records sampled from 14 acute care hospitals and compared clinically recorded data with administrative records from the Canadian Institute for Health Information. The impact of comorbidity coding in the administrative data set according to the Charlson classification was examined in models of 30-day mortality.

RESULTS

The positive predictive value (PPV) of a primary diagnosis ICD-9 428 was 94.3% using the Framingham criteria and 88.6% using criteria previously validated with pulmonary capillary wedge pressure. There was reduced prevalence of secondary comorbid conditions in administrative data in comparison with clinical chart data. The specificities and PPV/negative predictive values of administratively identified index comorbidities were high. The sensitivities of index comorbidities were low, but were enhanced by examination of hospitalizations within 1 year prior to the index heart failure admission. Using information from prior hospitalizations modestly enhanced 30-day mortality model performance; however, the odds ratio point estimates of the index and enhanced administrative data sets were consistent with the clinical model.

CONCLUSION

The ICD-9 428 primary diagnosis is highly predictive of heart failure using clinical criteria. Examination of hospitalization data up to 1 year prior to the index admission improves comorbidity detection and may provide enhancements to future studies of heart failure mortality.